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Depth map restoration based on Kinect camera

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  2018 ASABE Annual International Meeting  1800478.(doi:10.13031/aim.201800478)
Authors:   Yue Zhang, Qibing Zhu, Min Huang, Ya Guo
Keywords:   Alternating direction method of multipliers, color guided depth image restoration, Kinect depth maps, multi-channel weight function, robust non-convex function

Due to the instability of depth data, image noise, lack of depth information and other reasons, the quality of Kinect depth maps can easily be reduced. In this paper, a color-guided depth map restoration optimization model is proposed to address the issues mentioned in a unified framework based on the structure information from color and input depth. By adopting a robust non-convex function and multi-channel weight function to simulate the regularization term of optimization model, this method can be used to suppress texture copy artifacts caused by the inconsistency between depth discontinuities and color fringes. Then, a modified fast alternating direction method of multipliers is used to solve the optimization problem. Through comprehensive experiments on both simulated data and real data, the promising performance of the method is proved. Moreover, the results show that the proposed scheme is around two times faster than the conventional majorize-minimize solver method.

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